Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021

Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21–30 to 8–11 hours, while maintaining program effectiveness.

contributions of CICT while maintaining the effects of vaccines and other NPIs.The difference between the reported cases and the model-simulated curve was the estimated cases averted by CICT (Appendix Figure 1).We calculated the proportion of the disease burden averted by CICT by dividing the averted case estimate by the cumulative case total of this model-simulated curve.
We calculated the number of hospitalizations averted by multiplying the averted cases by the age-stratified infection-to-hospitalization ratio (5,6), Appendix Table 8.Lastly, we compared the two periods by examining the average staff hours per each averted case and the staff hours required to increase the averted disease burden by one percentage point.
Readers can use the publicly available tool (https://www.cdc.gov/ncezid/dpei/resources/covid-tracer-Advanced-Special-edition.xlsm) and the instructions provided in Rainisch et al. (2) to replicate the analysis for their respective jurisdiction.

Calculating CICT Effectiveness
We defined the effectiveness of the CICT program in terms of coverage (% of cases and contacts isolated and quarantined due to the program) and timeliness (number of days from exposure to isolation/quarantine).These effectiveness values were calculated using field-based data, such as the proportion of cases that completed case interviews (Table 1), as well as assumed values, such as public compliance with isolation and quarantine guidelines (Appendix Table 1).We assumed that a certain proportion of confirmed cases are effectively isolated following case interviews.We further assumed that a certain proportion of contacts are quarantined either upon contact notification or through active monitoring.
We calculated the average proportion of cases and contacts isolated and quarantined by the CICT program as follows: Here,  1 represents the % of interviewed cases that isolated,  2 represents the % of monitored contacts that quarantined, and  3 represents the % of notified (but not monitored) contacts that quarantined.The multiplier k accounts for the expectation that the known case count represents just a fraction of the total secondary cases during our study period since undetected infected contacts would have further infected additional individuals.Therefore, we used an approximation of the effective reproduction number (Re) during our study period for the value of k: k = 1.2.If k>1 (indicating an outbreak is growing), the proportion of contacts identified has a larger impact on the overall CICT effectiveness compared to the proportion of cases interviewed.Conversely, if k<1 (indicating an outbreak is waning), the proportion of cases interviewed has a larger impact on the overall CICT effectiveness.During the evaluation period, the average Re in Philadelphia was 1.29 and 0.99 during Periods 1 and 2, respectively.Therefore, using a single value of k = 1.2 was deemed sufficient as a proxy over the short period of time we analyzed.
The number of days from exposure to isolation/quarantine was determined by calculating the average number of days to case isolation and contact quarantine.We assumed that cases experience a 5-day pre-symptomatic period (7,8).To obtain the number of days from symptom onset to case interview, we added the reported "Average days from symptom onset to specimen collection" and the "Average days from specimen collection to case interview".Additionally, we assumed that confirmed cases begin isolation the day after their interview (i.e., we added 1 to the total obtained above).
For contacts, we assumed they begin quarantine the day after receiving exposure notification from their health department.Since information on the actual dates of exposure for contacts was not available, we assumed that these individuals' exposures occurred at the midpoint of their potential exposure window (in days).We identified the earliest date in this window as the first day of infectiousness among cases to which contacts were exposed.We identified the latest possible exposure as the date the cases exposing them were interviewed by the health department (because they began isolation the next day).To calculate the number of days from contacts' exposure to their quarantine, we took the average of the maximum days a contact was infected and the fewest days the contact could be infected and weighted each day span by the case's infectiousness on each of the possible exposure days.Appendix Figure 2 illustrates the timing of exposure to isolation/quarantine for Philadelphia before the CICT protocol change, based on the aforementioned assumptions and the reported CICT performance metrics.

Defining the Susceptible Population and Accounting for Vaccination and Waning Immunity
The COVIDTracer modeling tool requires inputs to define the susceptible population.
Individuals can be protected against infection through either vaccination or prior infection; however, immunity wanes over time.We assumed that both naturally acquired and vaccineinduced immunity last for 180 days.We also assumed no partial immunity (i.e., individuals are either fully protected or fully susceptible) during the evaluation period.We further assumed the likelihood of getting vaccinated is the same among the previously infected and uninfected individuals.
Based on these assumptions, we estimated the "fully protected" population as follows: • Those fully vaccinated within 180 days of the evaluation period's start date • Individuals who received a booster dose • Those who were vaccinated 180 days ago or more (and thus lost immunity), but infected within 180 days • Individuals who were unvaccinated but were infected within 180 days The susceptible population is calculated by subtracting the "fully protected" population from the city's total population.
Those infected with the Delta variant also appear to have a shorter latent period (days from exposure to being infectious), becoming infectious as early as 2 days post-exposure, compared to 3 days among those infected with variants in circulation before Delta's dominance (11,12).Without commensurate improvements in the speed of contact notification, a shorter latent period will contribute to a diminished impact from CICT, as infected individuals can transmit the virus more quickly before the health department could reach and isolate them.
Therefore, to account for both the circulation of the Delta variant and other variants, we estimated the impact of CICT (cases and hospitalizations averted) under two scenarios: 1) cases become infectious 2 days post-exposure, and 2) cases become infectious 3 days post-exposure.
The former scenario provided a lower-bound estimate of CICT impact, while the latter provided an upper-bound estimate.

Sensitivity Analysis: Isolating effects of the protocol change
The two evaluation periods differed in various factors that could impact the performance of the CICT program.One notable difference was the mean daily incidence of COVID-19, which was twice as high during Period 2 due to the surge associated with the increased circulation of the Delta variant.In Period 2, the daily incidence was 18 cases per 100,000 population, while in Period 1, it was 9 cases per 100,000 population (Table 1).
To evaluate the isolated effects of the protocol change, we estimated the number of cases and hospitalizations averted in Period 2 (post-protocol change) assuming that the CICT protocol and its effectiveness remained unchanged from Period 1.Our analysis shows that the new protocol resulted in 93-189 fewer cases averted than would have occurred if the protocol had not changed (Appendix Table 3).This indicates that, during the evaluation period, the benefits of increased notification speed were not sufficient to fully offset the negative effects of the lower coverage.

Sensitivity Analysis: Potential effects of increased or decreased compliance with isolation and quarantine guidelines
If public compliance with isolation and quarantine guidelines was different than what we assumed in our baseline scenario (Appendix Table 1), the estimated number of cases and hospitalizations averted by CICT could have been 29% lower (low compliance) or 30% greater (high compliance) than the baseline scenario (Appendix Tables 4, 5).

COVIDTracer Modeling Tool, Overview and Assumptions
COVIDTracer is a spreadsheet-based tool that utilizes a Susceptible-Exposed-Infectious-Recovered (SEIR) epidemiologic model to illustrate the spread of a pathogen, the resulting disease, and the effects of interventions in a user-defined population (3).Interested readers can download the tool and enter input values of their choosing, exploring scenarios and assumptions beyond those covered in this manuscript.The tool can be accessed through the following link: https://www.cdc.gov/ncezid/dpei/resources/covid-tracer-Advanced-Special-edition.xlsm.
To simulate the clinical progression and transmission of disease using COVIDTracer, we used the following definitions and assumptions.A "case" was defined as an individual who had been exposed, infected, and subsequently became infectious, regardless of the presence of clinical symptoms.We assumed that cases do not infect others for the first 3 days after infection.
From days 4 to 5 post-infection, cases are pre-symptomatic but capable of shedding virus to infect others (7,8,13).From days 6 to 14, the infected individuals may experience symptoms and continue to shed virus, although the risk of onward transmission is relatively low during days 11 to 14.The complete infectivity distribution is outlined in Appendix Table 6.We assumed that ≈40% of cases were asymptomatic from days 6 to 14 but still posed a risk of onward transmission equivalent to 75% of symptomatic cases (Appendix Table 7) in the absence of vaccines or other non-pharmaceutical interventions (NPIs) (13).The model assumed homogeneous mixing among individuals and did not account for any age-or location-based heterogeneities in transmission (such as within and between households or schools), or variations in the effectiveness of vaccines and other NPIs over the study period.Furthermore, the tool used a deterministic model that did not account for uncertainties around parameters.Users are encouraged to alter the default parameter values and conduct sensitivity analyses to assess the impact of these assumptions (for reference, see (10,14) for a range of R0 values).
into quarantine on day 13 (based on the aforementioned assumptions).To calculate the days from contacts' exposure to their quarantine, we took the average of the maximum days a contact was infected (9 days, based on the earliest possible exposure) and the minimum days the contact could be infected (2 days, based on the latest possible exposure), and weighted each day span by the case's infectiousness on each of possible exposure day.The result was 5.9 days in this example.Subsequently, we calculated the average between 11 days (index case) and 5.9 days (contacts) as the number of days from exposure to isolation (for both cases and contacts), which totaled 8 days.This final value of 8 days represents one of the key CICT performance metrics, the number of days from exposure to isolation/quarantine.